Chat with us!We developed an AI recruitment platform that screens applications, matches candidates to roles on skills and fit, and automates scheduling — helping recrui...
We see better shortlists in a fraction of the time — and we can explain exactly why each candidate ranked where they did.
Head of Talent Acquisition
We developed an AI recruitment platform that screens applications, matches candidates to roles on skills and fit, and automates scheduling — helping recruiters focus on people instead of paperwork while reducing bias.
| Project Detail | Information |
|---|---|
| Project Type | Web |
| Industry | HR Tech |
| Technologies | Embedding models, LLM, pgvector, Next.js, Node.js, PostgreSQL |
| Delivery Partner | mTouch Labs |
| Primary Outcome | Recruiters moved faster and surfaced stronger, more diverse shortlists with consistent criteria. |
Recruiters were overwhelmed by application volume, strong candidates were missed in keyword filters, and manual screening introduced inconsistency and bias.
We used semantic matching to score candidates on actual skills and experience rather than keywords, paired with structured, criteria-based evaluation to improve fairness and consistency.
The platform ranks candidates with explainable match scores, generates structured screening summaries, and automates interview scheduling end to end.
Semantic candidate-role matching
Screening summaries and structured evaluation
Candidate similarity search
Recruiter dashboard and workflows
Matching and scheduling services
Candidate and pipeline data
Documented screening criteria and pipeline stages.
Built semantic scoring with explainability.
Added bias-mitigation rules and audit logging.
Integrated calendars for self-serve interview booking.
Back-tested rankings against past successful hires.
Recruiters moved faster and surfaced stronger, more diverse shortlists with consistent criteria.
AI-assisted recruitment let the team evaluate more candidates fairly and faster, keeping human judgment central to final decisions.
We built an autonomous AI support agent that resolves customer tickets end to end — understanding intent, retrieving account context, and taking real actio....
View case studyWe developed a generative AI chatbot that holds natural, context-aware conversations across a SaaS product — answering product questions, guiding onboardin....
View case studyWe delivered an AI workflow automation platform that orchestrates multi-step business processes across departments — routing documents, making decisions, a....
View case studyWhat businesses ask us most often about this project and how we built it.
It uses semantic embeddings to compare a candidate's real skills and experience against the role, producing an explainable match score rather than a keyword pass/fail.
No. The platform ranks and summarizes candidates to assist recruiters; final decisions remain with people.
We apply structured, criteria-based evaluation, bias-mitigation controls, and full audit logging to make screening more consistent and fair.
Yes. The talent-pool search lets recruiters re-discover strong candidates from previous roles.
Yes. It integrates with applicant tracking systems and calendars to automate scheduling and keep records in sync.
mTouch Labs combines AI-powered development with deep industry expertise to deliver solutions faster.
